Presentation to uci

© 2015 IBM

Presentation to University of California Irvine Dr. Arvind Sathi February 25, 2015

© 2015 IBM 2

The Dance Vacation Product Idea

A vacation for dance enthusiasts

Using the DWTS format

Complete with Disney costumes

On Disney Cruise Line

Con

cept

© 2015 IBM 3

What this scenario demonstrates

A high value, high margin business opportunity A micro-segment of customers which can not be reached via

broad marketing campaigns A combination of Disney and external data, correlated to

formulate the product, and the campaign A custom defined ecosystem which gets access to this product

and related campaigns A set of interactions geared towards specific micro-segments.

© 2015 IBM 4

Overview

Changing Winds Proposition 1: From “Sample recalls” to “Observing the Population” Proposition 2: Marketing through Collaborative Influence Proposition 3: From silo’ed to Orchestrated Marketing Technological Enablers Changes to Marketing Ecosystem and Organization

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Changing Winds

Rise of Digital Society Ubiquitous use of Mobile Platform Savvy customers discover Social Computing Crowd-sourced analytics tools Monetization Private and public clouds Customer preferences and privacy concerns

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How was your first marketing exposure to the Social Media?

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Internet of Things – Ecosystem Map from Beecham Research

Source: M2M/IoT Sector Map by Beecham Research

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Monetization of data – emergence of a market place

www.lumapartners.com, reprinted with permission

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Proposition 1: From “Sample recalls” to “Observing the Population” Census data Social media data Location data Product usage data Shopping data Conversation data Purchase data

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Data

Cell tower locations Wi-fi locations Device locations Device usage data – apps, web

sites Customer data – demographics

Refined locations Mobility Patterns Hang outs Hang outs correlated with business locations Mode of transportation Traveling buddies

Analytics

Location Data

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Discovery from location data •  A typical discovery uses statistical tools to identify pattern in data. •  Discovery may contribute new derived attributes for further analysis or reporting.

Night Owls at Night

Delivery People During the Day

Quiet Weekday peoplego for dinner on weekends

Almost no Homebodies any time

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Buddies, Hangouts, Sofa Surfers Three areas of analysis:

n  Subscriber level Lifestyle and Mobility profiles

n  Popular Locations with specific profiles

n  Subscriber Pairings or Buddies

Who Are You? Homebody Daily Grinder Delivering the Goods Globetrotter Sofa Surfer

10 Top Hangouts

Best Buddies ID Rank Night Morning Lunch Dinner Breakfast Afternoon Total Result

54796109xxx 1 34 7 11 15 9 12 88

54809186xxx 2 33 7 11 15 9 12 87

30931430xxx 3 32 7 11 15 9 12 86

54802704xxx 4 31 7 11 15 9 12 85

54796392xxx 5 29 5 11 15 6 11 77

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Competitive Locations Have Different Profiles of Traffic Throughout the Day

Location of Latte Land is very close to Starbucks, but has more evening traffic

Time of Day Store

Visits per interval

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Subscriber URL Activity Mined to Create Interest Profile

-  Use Social Media (Twitter) data to

create profiles §  Soccer: User interest in soccer,

favorite teams §  Telco: Services provided by Telco §  Others: Users viewing experience,

Users comments on Apps including what they like/dislike

-  Research URL Analytics asset and Tag Cloud asset

§  Identify categories user will be interested in based on URL analytics

§  Identify word clouds based on pages associated with category

Interest Profile

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System U / Deriving Personality Profile

Psycho-linguistic Profile

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Group with no leader

Social Network using Voice Call Data

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Slice and Dice of my purchase data

www.slice.com, reprinted with permission

© 2015 IBM 18

How can this be utilized by Marketers

Amazon Apple

iTunes

PayPal eBay

Target

Groupon Living Social

Netflix Google Play

Best Buy

Newegg

Walmart Zappos

Woot

Monoprice.com

www.slice.com, reprinted with permission

© 2015 IBM 19

Building Context and Intent from Location data

Deriving location: location information may be derived using multi-modal information •  CDR data, tower data, device data, Wi-fi etc. •  Accuracy of location information depends on data fidelity etc.

Building context: making sense of the location information •  Correlate location information with business data •  Various other correlation rules may be used to build a rich context

Inferring intent: infer consumer level intents by leveraging location and mobility patterns

Deriving Location Inferring Intent Building Context

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Proposition 2: Marketing through Collaborative Influence Personalized customer / product research

Online advertising Multi-channel shopping Intelligent campaigns Big ticket items and auction / negotiation markets Games, videos, smart phones and tablets Influence through crowd-sourced reviews Endorsements and viral “buzz”

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Customer Needs and Usage Mapped to Products

Customers Needs Usage Offerings Components

Micro Segment

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Customer Needs and Usage Mapped to Products

Customers Needs Usage Offerings Components

Day time Work at Home

Work day High Usage

Off time Low Usage

Home Office

Bandwidth

Network Policy

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A not so intelligent campaign

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Drive Interact with the customer to seek permission to use loca3on informa3on and send campaign, record interac3on and results.

Discover Collect historical behavioral data, past acts, and success rates. Analyze historical data to formulate pa?erns and changes required to detect, and inves3gate steps

Decide Use background informa3on, past campaigns, privacy preferences, customer reac3on to past campaigns, purchase intent, preferences expressed in social media to design campaign.

Detect Detect in real 3me if a transac3on relates to targeted subscribers. Iden3fy, align, score, and send for further processing (e.g., a targeted customer driving towards mall)

Smarter Campaigns using D4

Detect observa,ons about a target

Take ac,on in real ,me – when it

ma8ers

Find new targets by analyzing historical

data

Iden,fy pa8erns over ,me and ac,ons required

Drive

Detect

Discover

Decide

Target Subscriber

24

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Digital Advertising Marketplace

Publisher

Advertisers

Supply Side Platform (SSP)

Demand Side Platform (DSP)

Data Management Platform (DMP)

Represents publishers, and runs auctions for inventory in real-time, finding the highest bidder

Represents brands, and bids on auctions for inventory in real-time, finding the best price / consumer propensity match

Sources data wherever it can to help DSPs in particular to make better predictions about inventory so that they can be more certain about the likely customer intent, and therefore bid higher and secure more conversions.

© 2015 IBM 26

Google India advertisement goes viral

https://www.youtube.com/watch?v=gHGDN9-oFJE Published on Nov 13, 2013 Partitions divide countries, friendships find a way

(Use captions to translate the film in 9 languages including French, Malayalam and Urdu) The India-Pakistan partition in 1947 separated many friends and families overnight. A granddaughter in India decides to surprise her grandfather on his birthday by reuniting him with his childhood friend (who is now in Pakistan) after over 6 decades of separation, with a little help from Google Search.

Views It is a 3 minute, 32 second advertisement that would be considered too long for a conventional

advertisement. It shows the Google products being used in a “use case,” and it attracted more than 3 million viewers in the first three days it was posted.

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Proposition 3: From silo’ed to Orchestrated Marketing

Customer profile Entity resolution Personal privacy preference management Dynamic pricing Orchestration for context-based advertising and promotion Cross-channel co-ordination Market tests

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Dance Vacation product requires a single customer profile connecting diverse interests.

28

A vacation for dance enthusiasts

Using the DWTS format

Complete with Disney costumes

On Disney Cruise Line

Con

cept

D

ata

Ser

vice

s

Facebook posts Mobility patterns Hang outs Social circles

Linear views Non-linear views Likes Past responses

Past purchases Likes Shares

Past purchases Interests

Fan advocacy Dance Studio partnership

Ads via non-linear

Campaigns across touch points

Campaigns across touch points

Customer Profile

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A Multi-dimensional Customer Profile

A comprehensive data model should capture a wide range of multi- dimensional and comprehensive information, adequate to reflect the customer’s complete digital profile

Descriptive data

Interaction data

Real Time Alerts and NBA

Privacy and Contact Preferences

Contextual Multichannel Profile

Partner Sectors – 3rd Party Data

Attitudinal data

Sentiments Customer Experience Profile

Permissions & Data Privacy

QoS Scores

Behavioral Data

OTT Favorites Mobility Profiles Usage and ARPU Profile

Mobile Payments

Digital Account Portrait Digital Signatures Onboarding and Retention

Personalizations SmartHome Subscriptions Red Flags

Financial & Billing Profile

Customer Lifetime Value

Top Up Wallet

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Step 1: Identifying a high value target progressively

Annon. ID Profile Informa;on Source

AB1234 None

Annon ID Profile Informa;on Source

AB1234 Interested in certain types of phones

Website – Phone page

AB1234 Interest in a par,cular phone

Website – Search

Interested in 4G Phone Website

Use of Customer Profile in Digital Advertising

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Step 2: User visits their favorite News site (Increase Brand Experience)

Offer 1

Offer 2

Offer 3

SmartPhone advertisement w/ “Fashion” callout

4G benefits advertisement

Generic Offertel advertisement $1.50

$2.50

$12.00

Profile Information Source Offertel homepage view Website

SmartPhone product page

Website

4G coverage eligible Website

4G Ad Creative Impression

Turn

4G Creative Ad Click Turn

Offertel landing page view Website

SmartPhone price plans Website

Video Ad


© 2015 IBM 32

Step 3: User visits multiple sites and eventually purchases item

Offer 1

Offer 2

Offer 3

“Premium Price Plan”

4G benefits advertisement

Generic Offertel advertisement $3.50

$4.50

$22.00

Profile Information Source Offertel homepage view

Website

High Income Profit Data Vendor/Telco

SmartPhone page Website

4G coverage eligible Website

4G Ad Creative Multiple websites

4G Creative Ad Click Multiple websites

Offertel landing page Website

SmartPhone price plans

Website

Ad Ad


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Technological Enablers

Volume, Variety, Velocity, Veracity of data Stream computing to address velocity Analytics and storage on MPP platforms for large volumes High variety data analytics Pattern discovery Adaptive intelligence Customer veracity and identity resolution Hybrid solution architectures

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Gartner Definition and Trends Gartner defines advanced analytics as, "the analysis of all kinds of data using sophisticated quantitative methods (for example, statistics, descriptive and predictive data mining, simulation and optimization) to produce insights that traditional approaches to business intelligence (BI) — such as query and reporting — are unlikely to discover.” An advanced analytics platform provides a full suite of tools for a knowledgeable user to perform a variety of analyses on different types of data. In today's market much of this analysis is predictive in nature, although elements of descriptive analysis are not uncommon. While these capabilities remain important, Key Disruptive Trends: Growing interest in applying the results of advanced analytics to improve business performance

is rapidly expanding the number of potential applications of this technology and its audience across the organization.

The rapid growth in available data, particularly new sources of data — such as unstructured data from customer interactions and streaming volumes of machine-generated data — require greater levels of sophistication from users and systems to be able to capture their full potential.

The growing demand for these types of capabilities is outpacing the supply of expert users, requiring significantly higher levels of productivity from skilled users as well as increasing the demand for "non-data-scientist-friendly" tools.

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Gartner Dimensions Dimension Description

Data Access Code-free basic data integration; advanced data integration; service-oriented architecture (SOA), Web data integration; basic extraction, transformation and loading (ETL) functionality; advanced ETL functionality; enterprise application access; data refresh; supported (for example, multimedia) data types; data lineage; data mashup; geospatial data and consumer data integration; geocoding;

limitations.

Visualization and Exploration / Discovery

Basic charts; advanced visualization chart types; export of visualizations into reports and Web-portals; advanced visualization GUI features; univariate and bivariate statistics; statistical significance testing; online analytical processing (OLAP), visual interaction and exploration.

Data Filtering and Manipulation

Binning and smoothing; feature generation dimensionality reduction and feature selection; filter and search, rotation, aggregation and set operations; transformations; signal preprocessing; custom mappings; dataset partitioning.

Advanced Descriptive Analytics

Clustering and self-organizing maps; affinity and graph analysis; conjoint and survey analysis; density estimation; similarity metrics.

Predictive Analytics Regression modeling; time-series analysis; neural nets; classification and regression trees; further rule-induction techniques; support vector machines; instance-based approaches; Bayesian modeling; ensembles and hierarchical models; import, call and development of other predictive models; measures of fit; testing of predictive models.

Optimization Solver approaches; heuristic approaches; design of experiments.

Simulation Discrete events, Monte Carlo simulation; agent-based modeling.

Further Advanced Analytics

Basic text analytics; text processing; vocabulary, language and ontology management; advanced text analytics; multimedia analytics; geospatial analysis; financial modeling and econometrics; signal processing and control.

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Gartner Dimensions (Continued) Dimension Description

Analytical Use Cases Marketing; sales; risk management and quality management; others.

Delivery, Integration, and Deployment

Integration; write-back; Web deployment and info graphics/dashboards; portal support; embedded delivery.

Platform and Project Management

Metadata management; model management; model licensing issues; decision management; scripting and automation; objects reuse; multiuser capabilities; debugging and unit testing; runtime optimization; audit and logs; data encryption; client deployment; extensibility.

User Experience Ease of use; documentation; guidance; wizards and contextual aids; user community; third-party applications.

Performance and Scalability

Big data, in-memory, in-database techniques; data volume scalability; algorithmic efficiency; real-time data and streams.

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Identity Resolution •  Identity resolution provides a way to connect various facts about an entity and resolve

differences.

[email protected]

Job Applicant

Identity Thief

Top 200 Customer

Criminal Investigation

© 2015 IBM 41

Changes to Marketing Ecosystem and Organization

Media planning and research Personalized marketing actions and impact on advertising and promotion organizations Refined product management for orchestrated marketing Data scientists – where do they belong? Infrastructure, data, or analytics as a service New role for marketing communications department Evolution vs. revolution

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Direct Negotiations in the Broadcast Era

Business Model

Media Formats

Audiences Advertiser

Radio

TV

Print

Billboard

Direct Negotiations

© 2015 IBM 43

Massive Audience Fragmentation and Auction Markets

Business Model

Social

Search

Radio

Video

Media Formats

Auction Markets

Smartphones

Devices

Digital Billboards

Connected TVs

Computers

Tablets

Audiences Advertiser

Display

Apps

© 2015 IBM 44

Summary

Changing Winds Proposition 1: From “Sample recalls” to “Observing the Population” Proposition 2: Marketing through Collaborative Influence Proposition 3: From silo’ed to Orchestrated Marketing Technological Enablers Changes to Marketing Ecosystem and Organization

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